ray/rllib/env/wrappers/atari_wrappers.py

from collections import deque
import gym
from gym import spaces
import numpy as np

from ray.rllib.utils.images import rgb2gray, resize


def is_atari(env):
    if (
        hasattr(env.observation_space, "shape")
        and env.observation_space.shape is not None
        and len(env.observation_space.shape) <= 2
    ):
        return False
    return hasattr(env, "unwrapped") and hasattr(env.unwrapped, "ale")


def get_wrapper_by_cls(env, cls):
    """Returns the gym env wrapper of the given class, or None."""
    currentenv = env
    while True:
        if isinstance(currentenv, cls):
            return currentenv
        elif isinstance(currentenv, gym.Wrapper):
            currentenv = currentenv.env
        else:
            return None


class MonitorEnv(gym.Wrapper):
    def __init__(self, env=None):
        """Record episodes stats prior to EpisodicLifeEnv, etc."""
        gym.Wrapper.__init__(self, env)
        self._current_reward = None
        self._num_steps = None
        self._total_steps = None
        self._episode_rewards = []
        self._episode_lengths = []
        self._num_episodes = 0
        self._num_returned = 0

    def reset(self, **kwargs):
        obs = self.env.reset(**kwargs)

        if self._total_steps is None:
            self._total_steps = sum(self._episode_lengths)

        if self._current_reward is not None:
            self._episode_rewards.append(self._current_reward)
            self._episode_lengths.append(self._num_steps)
            self._num_episodes += 1

        self._current_reward = 0
        self._num_steps = 0

        return obs

    def step(self, action):
        obs, rew, done, info = self.env.step(action)
        self._current_reward += rew
        self._num_steps += 1
        self._total_steps += 1
        return (obs, rew, done, info)

    def get_episode_rewards(self):
        return self._episode_rewards

    def get_episode_lengths(self):
        return self._episode_lengths

    def get_total_steps(self):
        return self._total_steps

    def next_episode_results(self):
        for i in range(self._num_returned, len(self._episode_rewards)):
            yield (self._episode_rewards[i], self._episode_lengths[i])
        self._num_returned = len(self._episode_rewards)


class NoopResetEnv(gym.Wrapper):
    def __init__(self, env, noop_max=30):
        """Sample initial states by taking random number of no-ops on reset.
        No-op is assumed to be action 0.
        """
        gym.Wrapper.__init__(self, env)
        self.noop_max = noop_max
        self.override_num_noops = None
        self.noop_action = 0
        assert env.unwrapped.get_action_meanings()[0] == "NOOP"

    def reset(self, **kwargs):
        """Do no-op action for a number of steps in [1, noop_max]."""
        self.env.reset(**kwargs)
        if self.override_num_noops is not None:
            noops = self.override_num_noops
        else:
            # This environment now uses the pcg64 random number generator which
            # does not have randint as an attribute only has integers.
            try:
                noops = self.unwrapped.np_random.integers(1, self.noop_max + 1)
            # Also still support older versions.
            except AttributeError:
                noops = self.unwrapped.np_random.randint(1, self.noop_max + 1)
        assert noops > 0
        obs = None
        for _ in range(noops):
            obs, _, done, _ = self.env.step(self.noop_action)
            if done:
                obs = self.env.reset(**kwargs)
        return obs

    def step(self, ac):
        return self.env.step(ac)


class ClipRewardEnv(gym.RewardWrapper):
    def __init__(self, env):
        gym.RewardWrapper.__init__(self, env)

    def reward(self, reward):
        """Bin reward to {+1, 0, -1} by its sign."""
        return np.sign(reward)


class FireResetEnv(gym.Wrapper):
    def __init__(self, env):
        """Take action on reset.

        For environments that are fixed until firing."""
        gym.Wrapper.__init__(self, env)
        assert env.unwrapped.get_action_meanings()[1] == "FIRE"
        assert len(env.unwrapped.get_action_meanings()) >= 3

    def reset(self, **kwargs):
        self.env.reset(**kwargs)
        obs, _, done, _ = self.env.step(1)
        if done:
            self.env.reset(**kwargs)
        obs, _, done, _ = self.env.step(2)
        if done:
            self.env.reset(**kwargs)
        return obs

    def step(self, ac):
        return self.env.step(ac)


class EpisodicLifeEnv(gym.Wrapper):
    def __init__(self, env):
        """Make end-of-life == end-of-episode, but only reset on true game over.
        Done by DeepMind for the DQN and co. since it helps value estimation.
        """
        gym.Wrapper.__init__(self, env)
        self.lives = 0
        self.was_real_done = True

    def step(self, action):
        obs, reward, done, info = self.env.step(action)
        self.was_real_done = done
        # check current lives, make loss of life terminal,
        # then update lives to handle bonus lives
        lives = self.env.unwrapped.ale.lives()
        if lives < self.lives and lives > 0:
            # for Qbert sometimes we stay in lives == 0 condtion for a few fr
            # so its important to keep lives > 0, so that we only reset once
            # the environment advertises done.
            done = True
        self.lives = lives
        return obs, reward, done, info

    def reset(self, **kwargs):
        """Reset only when lives are exhausted.
        This way all states are still reachable even though lives are episodic,
        and the learner need not know about any of this behind-the-scenes.
        """
        if self.was_real_done:
            obs = self.env.reset(**kwargs)
        else:
            # no-op step to advance from terminal/lost life state
            obs, _, _, _ = self.env.step(0)
        self.lives = self.env.unwrapped.ale.lives()
        return obs


class MaxAndSkipEnv(gym.Wrapper):
    def __init__(self, env, skip=4):
        """Return only every `skip`-th frame"""
        gym.Wrapper.__init__(self, env)
        # most recent raw observations (for max pooling across time steps)
        self._obs_buffer = np.zeros((2,) + env.observation_space.shape, dtype=np.uint8)
        self._skip = skip

    def step(self, action):
        """Repeat action, sum reward, and max over last observations."""
        total_reward = 0.0
        done = None
        for i in range(self._skip):
            obs, reward, done, info = self.env.step(action)
            if i == self._skip - 2:
                self._obs_buffer[0] = obs
            if i == self._skip - 1:
                self._obs_buffer[1] = obs
            total_reward += reward
            if done:
                break
        # Note that the observation on the done=True frame
        # doesn't matter
        max_frame = self._obs_buffer.max(axis=0)

        return max_frame, total_reward, done, info

    def reset(self, **kwargs):
        return self.env.reset(**kwargs)


class WarpFrame(gym.ObservationWrapper):
    def __init__(self, env, dim):
        """Warp frames to the specified size (dim x dim)."""
        gym.ObservationWrapper.__init__(self, env)
        self.width = dim
        self.height = dim
        self.observation_space = spaces.Box(
            low=0, high=255, shape=(self.height, self.width, 1), dtype=np.uint8
        )

    def observation(self, frame):
        frame = rgb2gray(frame)
        frame = resize(frame, height=self.height, width=self.width)
        return frame[:, :, None]


# TODO: (sven) Deprecated class. Remove once traj. view is the norm.
class FrameStack(gym.Wrapper):
    def __init__(self, env, k):
        """Stack k last frames."""
        gym.Wrapper.__init__(self, env)
        self.k = k
        self.frames = deque([], maxlen=k)
        shp = env.observation_space.shape
        self.observation_space = spaces.Box(
            low=0,
            high=255,
            shape=(shp[0], shp[1], shp[2] * k),
            dtype=env.observation_space.dtype,
        )

    def reset(self):
        ob = self.env.reset()
        for _ in range(self.k):
            self.frames.append(ob)
        return self._get_ob()

    def step(self, action):
        ob, reward, done, info = self.env.step(action)
        self.frames.append(ob)
        return self._get_ob(), reward, done, info

    def _get_ob(self):
        assert len(self.frames) == self.k
        return np.concatenate(self.frames, axis=2)


class FrameStackTrajectoryView(gym.ObservationWrapper):
    def __init__(self, env):
        """No stacking. Trajectory View API takes care of this."""
        gym.Wrapper.__init__(self, env)
        shp = env.observation_space.shape
        assert shp[2] == 1
        self.observation_space = spaces.Box(
            low=0, high=255, shape=(shp[0], shp[1]), dtype=env.observation_space.dtype
        )

    def observation(self, observation):
        return np.squeeze(observation, axis=-1)


class ScaledFloatFrame(gym.ObservationWrapper):
    def __init__(self, env):
        gym.ObservationWrapper.__init__(self, env)
        self.observation_space = gym.spaces.Box(
            low=0, high=1, shape=env.observation_space.shape, dtype=np.float32
        )

    def observation(self, observation):
        # careful! This undoes the memory optimization, use
        # with smaller replay buffers only.
        return np.array(observation).astype(np.float32) / 255.0


def wrap_deepmind(env, dim=84, framestack=True):
    """Configure environment for DeepMind-style Atari.

    Note that we assume reward clipping is done outside the wrapper.

    Args:
        env (EnvType): The env object to wrap.
        dim (int): Dimension to resize observations to (dim x dim).
        framestack (bool): Whether to framestack observations.
    """
    env = MonitorEnv(env)
    env = NoopResetEnv(env, noop_max=30)
    if env.spec is not None and "NoFrameskip" in env.spec.id:
        env = MaxAndSkipEnv(env, skip=4)
    env = EpisodicLifeEnv(env)
    if "FIRE" in env.unwrapped.get_action_meanings():
        env = FireResetEnv(env)
    env = WarpFrame(env, dim)
    # env = ScaledFloatFrame(env)  # TODO: use for dqn?
    # env = ClipRewardEnv(env)  # reward clipping is handled by policy eval
    # 4x image framestacking.
    if framestack is True:
        env = FrameStack(env, 4)
    return env